At the same time, the XXI century has given birth to an immense amount of measurement data, which is also available as public datasets in various scientific domains, including structural mechanics and material science [1]}, [2]}, [3]}. The abundant data availability and rapid growth in computational resources have given rise to machine learning (ML) techniques [4]}, [5]}, [6]} which are gaining increasing importance in solving forward and inverse engineering problems. This includes data-driven approaches aiming to enable modeling [7]}, to accelerating computationally costly direct numerical simulations [8]}, [9]}, [10]} or even to discovering new material laws [11]}, [12]}.
